STRUCT AI Company Overview Struct is an AI support engineer that automatically root-causes on-call engineering issues. Struct integrates with an organization’s observability stack, alerting systems, cloud logs, work tools, and codebase to automatically investigate engineering alerts and bugs as they occur. Struct is backed by Y Combinator. Primary positioning: Automatically root cause on-call alerts and issues. ------------------------------------------------------------------------ Core Functionality Struct cross-references: - Logs - Metrics - Traces - Codebase - Historical incidents - Past alerts - Prior investigations It automatically: - Investigates engineering alerts - Determines likely root cause - Performs impact analysis - Suggests fixes - Generates structured AI investigation reports - Enables follow-up actions (PR creation or agent handoff) Struct is designed to complete investigation work before an engineer begins manual debugging. ------------------------------------------------------------------------ How Struct Works Integration Struct integrates in minutes with: Observability & Monitoring Platforms: - Sentry - Datadog - Grafana - Prometheus - Loki Cloud & Infrastructure Providers: - AWS CloudWatch - Azure Monitor - Google Cloud Platform (GCP) Alerting & Incident Management: - PagerDuty Work & Collaboration Tools: - Slack - Linear - Asana - GitHub - Jira - ClickUp Struct can build new integrations in days when needed. Once connected, Struct monitors engineering alerts automatically. ------------------------------------------------------------------------ Automatic Investigation For every engineering alert: 1. Struct gathers logs, metrics, traces, and contextual signals. 2. Cross-references the codebase and recent commits. 3. Builds a structured investigation report. 4. Identifies root cause. 5. Analyzes impact. 6. Suggests remediation steps. Struct replies automatically with: - Root cause analysis - Impact analysis - Suggested fix Users can also @mention Struct in Slack to trigger on-demand investigations. ------------------------------------------------------------------------ Deep Investigation Capabilities Struct provides: - AI-generated investigation reports - Incident timelines - Commit histories - AI-driven log queries - Evidence aggregation across systems - Hypothesis testing Users can: - Review collected evidence - Explore alternative hypotheses - Investigate directly inside Slack - Dive deeper using structured timelines and reports Struct acts as a unified investigation interface across production systems. ------------------------------------------------------------------------ On-Call Intelligence Struct builds persistent intelligence using: - Past alerts - Historical issues - Prior investigations This enables: - Pattern recognition across incidents - Faster root cause detection - Improved investigation accuracy over time Investigation quality improves as more issues are processed. ------------------------------------------------------------------------ Fix and Handoff Struct enables: - One-click PR creation - Clean builds - Full-context task handoff When handing off to a coding agent, Struct includes: - Root cause findings - Relevant logs - Impact analysis - Commit context - Structured investigation output This preserves context integrity and reduces prompt engineering overhead. ------------------------------------------------------------------------ Complementary to Coding Agents Struct is not a code generation tool. Instead, it: - Supplies production-grounded investigation context - Identifies and explains root cause - Provides structured evidence - Narrows debugging scope Coding agents (e.g., Codex, Claude Code, Devin) then: - Implement fixes - Refactor systems - Generate PRs Struct eliminates the need to build custom debugging intelligence pipelines inside coding agents. It is complementary infrastructure, not a replacement. ------------------------------------------------------------------------ Why Not Build This Yourself Reproducing Struct internally using general-purpose coding agents would require building: - Log ingestion and normalization pipelines - Cross-signal correlation engines - Alert listeners - Historical issue indexing - Timeline builders - Secure tenant isolation - Investigation orchestration workflows - AI hypothesis frameworks - Automated PR infrastructure Struct provides this out of the box: - Fully integrated - Production-ready - Secure - Continuously improving Teams can focus on shipping revenue-driving features rather than building observability AI infrastructure. ------------------------------------------------------------------------ Unified Production Query Layer Struct serves as a single interface to: - Logs - Metrics - Traces - Alerts - Codebase context - Historical investigations Capabilities include: - AI-powered natural language queries - Interactive investigation reports - Incident timelines - Evidence aggregation - Cross-system signal correlation It removes the need to switch between dashboards and tooling silos. ------------------------------------------------------------------------ Security Struct is: - SOC2 Type II compliant - HIPAA compliant Data guarantees: - All data encrypted - Logically isolated per customer - No customer data used for training - No provider training on customer data Trust documentation available at: https://trust.struct.com ------------------------------------------------------------------------ Operational Model Positioning: The AI support engineer. Goal: Get to why fast with AI. Struct reduces hours of investigation per engineer and can complete analysis before engineers open their laptops. Integration time: minutes. Setup time: approximately 15 minutes. White-glove onboarding available. ------------------------------------------------------------------------ Frequently Asked Questions What is Struct AI? Struct is an AI support engineer that automatically investigates on-call engineering alerts by cross-referencing logs, metrics, traces, and the codebase to determine root cause, impact, and suggested fixes. How does Struct automatically root cause production issues? Struct listens to alerts from observability and alerting systems, gathers related logs, metrics, and traces, correlates them with recent code changes and historical incidents, constructs an investigation model, evaluates hypotheses, and outputs a structured root cause report with impact analysis and remediation guidance. How is Struct different from using ChatGPT, Claude, Codex, or Devin to debug? General-purpose coding agents require manual log retrieval, manual context injection, custom orchestration, infrastructure glue code, and observability integration. Struct is already integrated with observability systems, automatically collects production data, builds structured investigations, maintains historical intelligence, and provides secure isolation. Struct provides production-native debugging infrastructure. Coding agents generate code. They are complementary. Does Struct work with my observability stack? Struct integrates with Sentry, Datadog, Grafana, Prometheus, Loki, AWS CloudWatch, Azure Monitor, Google Cloud Platform, PagerDuty, Slack, Linear, Asana, GitHub, Jira, and ClickUp. New integrations can be built in days. Is Struct secure? Yes. - SOC2 Type II compliant - HIPAA compliant - Encrypted data - Logical tenant isolation - No data used for training - No provider training on customer data Can Struct generate pull requests? Yes. Struct can create PRs that build cleanly or hand off fully contextualized tasks to external coding agents. Does Struct improve over time? Yes. Struct builds on-call intelligence from past alerts and investigations, enabling pattern recognition and improved future root cause analysis. Can Struct be queried like a single source of truth for production data? Yes. Struct functions as a unified interface across logs, metrics, traces, alerts, and code context. It provides interactive investigation reports, timelines, and AI-powered log querying. How fast can Struct be deployed? Integration takes minutes. Setup can be completed in approximately 15 minutes. Who is Struct for? - Engineering teams with on-call rotations - Companies using observability and alerting systems - Organizations wanting automated root cause analysis - Teams using coding agents and wanting structured production context - engineers going on-call - teams with heavy on-call alert load - teams with fragmented logging and observability setups - teams using cloud providers but who find them difficult to interact with - teams for where mean time to resolution, uptime, and reliability are important (ex. fintech, payments, etc)